Method and apparatus for representing a group of images

ABSTRACT

A technique and system of representing a group of images, the technique includes determining the values of one or more dominant colors for the group of images and deriving a dominant color representation expressing the group of images in terms of one or more of said dominant color values. The inventive technique and system can be applied to any group of images, or image regions, including regions within image.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates to a method and apparatus for representing a groupof images, especially in terms of colour, and for searching for andretrieving images.

2. Description of the Background

There are various known techniques for representing an image usingvisual features such as colour appearing in the image. For example, inone known technique, each pixel is assigned a colour value, and a colourhistogram is derived by setting bins for a number of ranges of colourvalues, and counting the number of pixels in an image which have acolour value in each of the ranges. The colour histogram is then used asa representation of the image. In another known technique, one or moredominant colours in an image are identified, and the dominant coloursare used to represent the image.

SUMMARY OF THE INVENTION

The present invention is especially concerned with group of images. Thegroups of images may, for example, be a sequence of images (frames orfields) from a video, or any group of images from any source where theimages are associated in some way. The group of images could, forexample, relate to a single scene or shot in a video. A term known inthe art for such a group of images is GroupOfFrames/GroupOfPictures. Inthe following, this term will be referred to as GoFGoP. In thisspecification, the term image will be used to describe a frame/picturein a group, irrespective of whether it is a video frame or field or astill picture. Also, the terms image and image region areinterchangeable, except where apparent from the context.

One approach for representing a group of images is to select a singleimage from the group of images, and treating the single image asrepresentative of the entire group. The single image is then representedusing a known technique for representing a single image. The singleimage may, for example, be the first or last image appearing in asequence, or the group of images may be analysed to identify an image inthe group that is in some way especially representative of the group, interms of the visual feature of interest.

Another approach is to aggregate the group of images. The existingMPEG-7 Visual Standard (ISO/IEC 15938-3) allows for the description ofcolour in a video segment or a group of pictures using a GoFGoP colourdescriptor. This is described in detail, for example, in the book:Introduction to MPEG-7 Multimedia content description interface Editedby Manjunath, Salembier and Sikora, ISBN 0-471-48678-7, section 13.5.Three techniques for aggregating a group of images are described:average, median and intersection.

In each technique, a colour histogram is derived for each image in thegroup, as described above. In the averaging technique, the colourhistograms are accumulated, and then each accumulated bin value isdivided by N, where N is the number of images in the group, to producean average histogram. In the median technique, for each bin value, thehistogram values for the group are arranged in ascending/descendingorder, and the median value is assigned to the respective bin. Theintersection histogram is obtained by, for each bin, taking the minimumhistogram value from the histograms for the group. In other words, theintersection histogram represents the number of pixels of a particularcolour or range of colours (corresponding to a bin) that appears in allof the images.

In each case, the aggregated histogram (average, median or intersection)is then represented using a Scalable Color Descriptor (section 13.4 ofthe book mentioned above) which involves applying a Haar transform-basedencoding scheme to the values of the color histogram.

The paper “Automatic video scene extraction by shot grouping” by TongLin and Hong-Jiang Zhang relates to the grouping of shots into scenes. Agroup of frames forming a shot is analysed to determine the dominantcolour objects in each frame, and then to determine the dominant colourobjects persisting throughout the group of frames. This produces adominant colour histogram for a shot. Dominant colour histograms fordifferent shots are compared. If the correlation between two shots ishigh, the shots are grouped into a scene.

Aspects of the invention are set out in the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

An embodiment of the invention will be described with reference to theaccompanying drawings of which:

FIG. 1 is a block diagram of a system according to an embodiment of theinvention;

FIGS. 2 a, 2 b and FIGS. 3 to 5 are graphs illustrating a method ofmerging of image descriptors according to an embodiment of theinvention;

FIG. 6 illustrates the combining of images to form a super-image.

DETAILED DESCRIPTION OF THE DRAWINGS

A system according to an embodiment of the invention is shown in FIG. 1.The system includes a control unit 12 such as a computer for controllingoperation of the system, the control unit 12 including at least a memoryand a processor, a display unit 14 such as a monitor, connected to thecontrol unit 12, for displaying outputs including images and text and apointing device 16 such as a mouse for inputting instructions to thecontrol unit 12. The system also includes an image database 18 storingdigital versions of a plurality of groups of images and a descriptordatabase 20 storing descriptor information, described in more detailbelow, for each of the groups of images stored in the image database 8.In this example, each group of images corresponds to a shot from a videosequence. There are various known techniques for dividing a videosequence into shots, which will not be described in detail here. Theinvention can be applied to any group of images, or image regions,including regions within images. The images may be stored in groups, or,for example, there may be an identifier indicating which images belongto the same group.

Each of the image database 18 and the descriptor database 20 isconnected to the control unit 12. The system also includes a searchengine 22 which is a computer program under the control of the controlunit 12 and which operates on the descriptor database 20.

In this embodiment, the elements of the system are provided on a singlesite, such as an image library, where the components of the system arepermanently linked.

The descriptor database 20 stores descriptors of all the images storedin the image database and additionally of all the groups of images inthe image database. The image and group descriptors are derived asdescribed below.

Each image has an associated image descriptor, which expresses therespective image in terms of the dominant colour or colours in theimage. In this embodiment, the image descriptors are derived essentiallyas described in our co-pending application WO 00/67203, the contents ofwhich are incorporated herein by reference.

A brief summary of deriving an image descriptor follows. Each image hasa plurality of pixels, and each pixel has an associated colour value, inthe relevant colour space, such as RGB.

The colour values and the corresponding pixels are clustered in thecolour domain in order to determine dominant colours and which colourscorrespond to a respective dominant colour. This is done using asuitable clustering algorithm, such as the Generalized Lloyd Algorithm,as described in section 13.3.1 of the MPEG-7 book mentioned above.

The cluster centroids resulting from the clustering procedure are usedas dominant colour values, and the sets of pixels that form theperspective clusters are stored for computation of additional fields(weight and colour variance) as discussed below.

Alternatively, the dominant colours may be derived using a histogramapproach, as described in W000/67203.

In that case, a colour histogram for an image is derived, by selecting apredetermined number of colour values, or ranges of colour values, inthe relevant colour space, and the number of pixels in the image havingeach colour value, or a value in the relevant range, is counted.

Generally, the histogram will have one or more peaks, and the peaks (ora subset thereof, such as a predetermined number of the highest peaks)are selected as the dominant colours and colour values/pixels areclustered in the colour domain with respect to the dominant colours.

Once the colour values have been clustered, a colour variance value isdetermined for each dominant colour, expressing for each dominant colourthe variance of the colour values for the respective cluster centred onthe dominant colour. The dominant colour can be considered as a meanvalue for the colour distribution in the relevant cluster.

The calculation of the variance can be expressed using the followingequation:

${CV}_{j} = {\frac{1}{N}{\sum\limits_{k = 0}^{N - 1}( {m_{j} - p_{kj}} )^{2}}}$where j indexes the color component, m_(j) is j-th component of thedominant color, p_(kj) is j-th component of the k-th pixel value, andthe summation is over N pixels corresponding to the dominant color underconsideration.

The descriptor also includes a weight value for each dominant colour,which is a measure of the relative significance of each dominant colourin the image. In this example, the weight is the ratio of the number ofpixels in the cluster corresponding to the dominant colour value to thetotal number of pixels in the image. The weight may be expressed as apercentage.

The dominant colour values, and their respective variances and weightsare combined to form a colour descriptor of the image. The descriptormay also have other components, such as a degree n, indicating thenumber of dominant colours. The descriptor may also include covariancevalues Cij, where i and j represent colour components in the relevantcolour space, for each dominant colour and cluster, as well as variancevalues.

The colour descriptor for each image is stored in the descriptordatabase.

It is important to note that a dominant colour descriptor is not thesame as a histogram representation of an image. A dominant colourdescriptor includes the values of the dominant colours in an image,which may have been determined in earlier processing steps. A histogramrepresentation of an image includes the dominant colours, but there isno identification of the dominant colours. The dominant colourdescriptor may also include other values, such as values correspondingto variance of colour distribution with respect to the or each dominantcolour, but a histogram representation does not involve calculating ordetermining the variance or other such values. Other components of adominant colour descriptor may include, for example, a weight indicatingthe influence of the dominant colour in the image, the number ofdominant colours in the image, and spatial homogeneity of pixelscorresponding to the dominant colors in the image.

A group descriptor for a group of images is derived as follows,according to a first embodiment.

The image descriptors for each image in the group of images is retrievedfrom the descriptor database. The image descriptors are then combined toform a group descriptor.

The group descriptor has a similar format to an image descriptor(dominant colours, variances, weights etc). Preferably, the number ofdominant colours in the group descriptor is between 1 and 8. However,the number of dominant colours may be unlimited or may be set with apredetermined maximum. The number of dominant colours in the imagedescriptor also may be unlimited or may be limited by a predeterminedmaximum. The maximum for the image descriptors and the maximum for thegroup descriptors may not necessarily be the same so that, for example,the image descriptors may have more dominant colours than the groupdescriptor.

In this example, there are two images in a group, and the two respectiveimage descriptors are combined as follows.

In general terms, the image descriptors are combined by merging clustersin the images based on proximity of the clusters in colour space.

FIGS. 2 a and 2 b are abstract representations of clusters in colourspace of the two images. FIG. 2 a represents the first image and FIG. 2b represents the second image. In each case, the circles represent acluster for a respective dominant colour. For simplicity, the colourspace is shown in two dimensions, although the colour space will usuallybe in three dimensions. Also, the clusters do not necessarily correspondto circles in colour space, but are shown as such for simplicity, andthe circles do not give any indication of the weight of the clusters. Inthe following, a representation such as in shown in FIGS. 2 a and 2 b(that is, a representation in terms of dominant colours and respectiveclusters) will be described as a cluster descriptor.

The two cluster descriptors of FIGS. 2 a and 2 b are combined to form acluster super-descriptor. This is illustrated in FIG. 3.

Next, the distance between each pair of clusters (as defined above) inthe super-descriptor is determined, using a suitable distancemeasurement in colour space. In this example, the distance is theEuclidean distance in colour space.

In this example, there are two dominant colours and two clusters in thefirst image, and three dominant colours and three clusters in the secondimage, numbered 1 to 5 respectively. Each dominant colour corresponds toa point in RGB colour space, represented by a cross in FIGS. 2 to 5. Thedistance between a pair of clusters is the distance in 3-D RGB spacebetween the centroids of the clusters. The distance between each pair ofdominant colours in the first and second images is calculated including,in this example, clusters from the same image. The pair of dominantcolours giving the smallest distance measurement are selected.

Next, the clusters corresponding to the two dominant colours are merged.

In this example, as shown in FIG. 3, the two closest clusters areclusters 1 and 3, and these are merged as follows.

The dominant or representative colour value of the merged cluster is aweighted average of the dominant colours of the two clusters, where theweight is as defined above. Thus, for two dominant colours m₁, m₂, andrespective weights W1 and W2, the merged dominant colour m has value:m=w ₁ m ₁ +w ₂ m ₂where w₁, w₂ are the relative weights, W1/W1+W2 and W2/W1+W2respectively.

The variance of the merged cluster is also calculated, using thevariances of the two clusters merged together. In this example, eachcolour component is treated independently, and it is assumed that thevariance of the merged cluster is a weighted sum of two Gaussiandistributions. This results in the following formula for the variance ofthe merged cluster:σ² =w ₁σ₁ ² +w ₂σ₂ ² +w ₁ w ₂(m ₁ −m ₂)²,where σ₁ ², σ₂ ², are the variances of the component clusters, m₁, m₂are their means and w₁, w₂ are the relative weights, as defined above.

The weight W of the merged cluster is W1+W2.

The merged cluster is treated as a new cluster, with weight, mean andvariance as explained above.

This is illustrated in FIG. 4, where clusters 1 and 3 are merged to forma new cluster 6.

Next, there is another iteration of the merging steps. The two clusters1 and 3 that were merged in the first iteration are excluded fromfurther consideration, and replaced by the merged cluster 6, as shown inFIG. 4.

The merging steps are then repeated, by identifying the closest pair ofclusters, including the merged cluster 6, in colour space and mergingthem, as outlined above.

In this example, in the second iteration, clusters 2 and 4 are theclosest pair of clusters. These are merged to produce a new cluster 7,with dominant colour, weight and variance derived as set out above fromthe dominant colours, weights and variances of clusters 2 and 4. Themerged cluster 7 replaces clusters 2 and 4, as shown in FIG. 5.

The merging iterations are repeated until a predetermined condition ismet. For example, the predetermined condition may be that merging iscontinued until a predetermined number of total clusters (sum of mergedclusters, and original remaining clusters in the first and secondimages) remains. Alternatively, the predetermined condition may be thatthe merging is continued until the distance between each remaining pairof clusters is greater than a given value. Alternatively, the method mayinvolve a predetermined number of iterations. More than one of thepredetermined conditions may be combined.

In the present case, the merging is repeated until a predeterminednumber (three) of clusters are remaining.

In the above example, merged clusters are considered in furtheriterations. However, they may be excluded from further iterations. Also,in the above example, clusters may be merged with other clusters withinthe same image, but in an alternative, clusters may be merged only withclusters appearing in another image, in the first and/or any subsequentiteration. This reduces the number of distance measurements.

Although described in terms of clusters, it is understood that themerging operates on the values in the descriptors ie dominant colour,variance and weight and it is not necessary to analyse the clustersthemselves.

Once the iterations have been completed, the remaining clusters are usedto form a group descriptor. More specifically, for each final cluster,there is a representative or dominant colour, a respective variance, anda respective weight. These are combined, together with a degree mindicating the number of final clusters, to form a group descriptor. Thegroup descriptor may also include other factors, such as an indicationof the colour space, or colour quantization used in the representation.The group descriptor is a GoFGoP dominant colour descriptor.

In the above example, there are only two images in the group of images.However, the method is also applicable to groups containing more thantwo images. The cluster descriptors for each image in the group couldall be combined, to form a super descriptor, as above. Alternatively,the group of images could be combined in sub-groups, for example, ingroups of two or three, in sequence or not in sequence, and then thedescriptors for the sub-groups combined in a similar way.

Where there are a large number of images in a group, the above methodpotentially involves a large number of clusters, and a large number ofcalculations of distances between clusters.

In view of the above, a variation of the above method takes account ofthe fact that most images in a video shot or a collection will be verysimilar and the corresponding descriptors will be similar. This meansthat most clusters can be merged on a per-frame basis withoutsignificant loss of precision.

In more detail, the variation considers the images of a group of imagesin a sequence. As in the example described above, the image descriptorshave already been derived and are stored in the descriptor database. Thecluster descriptors for a first and second image in a group of imagesare retrieved. Next, the distances between each cluster in the pair ofimages are determined, If the distance between any pair of clusters isbelow a predetermined threshold, the pair of clusters are merged. Themerged clusters and any remaining clusters in the pair of images arecollected in a super-descriptor, as in the example above. The clusterdescriptor for the next image is then retrieved and merged withsuper-descriptor for the first two images in the same way, by mergingclusters that are close, and forming a new super-descriptor. When thecluster descriptors for all the images have been considered, theresulting super-descriptor is merged using all remaining clusters as setout in the first example.

In the above description, the cluster descriptors have already beenderived. Alternatively, the image may be retrieved or supplied, and thedescriptor derived from the image before performing the cluster merging.

In the first embodiment described above, a descriptor of a GoFGoP isderived by aggregating the descriptors for each image in the group.

In a second embodiment, the images are aggregated in the image or pixeldomain, and then a dominant colour descriptor is derived from theaggregated image to produce a GoFGoP dominant colour descriptor. Thus,if there are N images 8 in the group, each image containing m×m pixels,the super-image 9 can be considered as an (N×m)×m array of pixels, asshown in FIG. 6.

In contrast to the first embodiment, the second embodiment does not usethe image descriptors for each image, but works directly with theimages.

After the images have been aggregated, a dominant colour descriptor isderived from the super-image, using the technique described above inrelation to the first embodiment.

An advantage of the second embodiment is that there is no loss ofprecision in the extraction process. However, a large amount ofcomplexity, especially memory, may be required in considering all theimages. To overcome the problem of complexity, the images may betemporally and/or spatially sub-sampled.

In the above, the images are aggregated in the image or pixel domain,and then a dominant colour descriptor is derived from the aggregatedimage to produce a GoFGoP dominant colour descriptor. Alternatively,each image could be aggregated in the colour domain (for example, in theform of a histogram for each image) and the dominant colour descriptorderived from the aggregated histograms. For example, a colour histogramfor each image may be derived, or may be retrieved from a memory. Next,the colour histograms are combined by adding them together to form asuper-image histogram, which may be normalised by the number of imagesforming the super-image. Finally, a dominant colour descriptor isderived from the super-image histogram, using the technique describedabove in relation to the first embodiment. In other words, the peaks(dominant colours) of the super-image histogram are selected, as well asthe respective variances and weights. Other techniques for aggregatingthe images before deriving the dominant colour descriptor may used. Forexample, instead of adding together the histograms for each image, theaverage, median or intersection of the group of images may becalculated. The dominant colour group descriptor is then derived fromthe resulting average, median or intersection histogram.

In a variation of the second embodiment, which applies to aggregation inthe image/pixel domain and in the colour/histogram domain, the group ofimages are temporally sub-sampled, as described below. In this example,the cluster descriptors for each image is derived or retrieved. It isdecided which images in the group to use in deriving the GoFGoPdescriptor on the basis of similarity of the cluster descriptors.

The first image in a sequence from the group of images forms the initialsuper-image. The following images are discarded until the similaritybetween the cluster descriptor of an image and the last image added tothe super-image (initially the first image) fulfils a predetermineddecision condition. If an image meets the condition, it is added to thesuper-image. The following images in the group are then compared withthe latest image added to the super-image until the predeterminedcondition is met again, and so on until all the images in the group havebeen considered. The dominant colour descriptor is then derived from theresulting super-image.

One possible decision criterion for image similarity is the value of thematching function between the respective DominantColor descriptors, suchas described in our co-pending application WO00/67203, or using amatching function as described in the MPEG-7 book mentioned above.Another criterion could be the result of “on-line” merging describedabove. The “decision criterion” in this case would be fulfilled if allclusters have been merged into the existing descriptor. Another approachthat would avoid extracting DominantColor for all images would be tocompute a crude colour histogram and use the histogram matching functionvalue as the criterion.

Both of these criteria require additional parameters to be specified: athreshold below which the matching function value is considered small inthe first case, and merging threshold in the second case. An alternativeapproach, which would be particularly applicable in case of limitedmemory, would be to adapt the threshold so that a the number of imagescollected does not exceed a specified limit.

A third embodiment derives a GoFGoP dominant colour descriptor from thegroup of dominant colour descriptors for the group of images. Morespecifically, for a group of images, the respective dominant colourdescriptors are retrieved from the descriptor database (or derived ifthey have not already been derived).

For each dominant colour descriptor, the distance between the descriptorand each of the remaining descriptors for the group is measured. Thisresults in a set of distance measurements for each descriptor, which maybe added together to give an overall distance measurement for eachdescriptor. The descriptor which has the smallest overall distancemeasurement is selected as a representative descriptor, and is treatedas the GoFGoP dominant colour descriptor.

Other ways of selecting the representative descriptor may be used,preferably involving tests or comparisons involving at least some of theimage descriptors for the images in the group. As another example, thiscould be done based on the distortion measure as defined in MPEG-7 ifdone for whole descriptors.

It is possible to pre-reject very close descriptors to reduce thecomputation. Various methods are set out above for deriving a GoFGoPdescriptor, especially a GoFGoP dominant colour descriptor.

There are various uses for the GoFGoP descriptor, such as in searchingfor and retrieving groups of images. For example, a user may wish tosearch for groups of images corresponding to an input image or group ofimages.

An outline of a search method is set out below.

Referring to FIG. 1, a query image is input by a user using suitablemeans such as a scanner or a digital camera, or by selecting a queryimage from a range of images displayed by the computer, or by selectinga region of any such images. A dominant colour descriptor for the imageis derived, as described above. The query dominant colour descriptor isthen compared with each of the GoFGoP dominant colour descriptors storedin the descriptor database. The GoFGoP dominant colour descriptors arein the same format as a single image dominant colour descriptor, and somatching can be performed, for example, using a matching function as setout in WO 00/67203 or similar, or a matching function as set out insection 13.3.2 of the MPEG-7 book mentioned above. The query descriptormay optionally also be compared with single image descriptors that maybe stored in the database.

The results of the matching function are ordered, and the groups ofimages for which the matching function indicates the closest matches areretrieved. One or more images from the closest matches may be displayed.

Other methods of posing a query may be used. A query may be posed byselecting a group of images and extracting a GoFGoP descriptor asdescribed above for the group. The group can be selected explicitly, forexample, by selecting a range of frames or implicity, for example, byselecting a keyframe in a video, where a “shot” including the keyframeis then derived using a suitable algorithm.

A system according to the invention may, for example, be provided in animage library. Alternatively, the databases may be sited remote from thecontrol unit of the system, connected to the control unit by a temporarylink such as a telephone line or by a network such as the Internet. Theimage and descriptor databases may be provided, for example, inpermanent storage or on portable data storage media such as CD-ROMs orDVDs.

The system described above as an embodiment of the invention is in theform of a computer system. The computer system may be a standardcomputer which has been programmed using suitable programs for executinga method according to an embodiment of the invention. The programs maybe stored in any suitable storage medium including fixed or permanentstorage or removable storage means. The system may be modified usingspecific hardware and/or software, including, for example, specificchips. The invention may also be implemented in a specifically adaptedapparatus, including specific hardware and/or software.

In the above description, the colour representations have been describedin terms of red, green and blue colour components. Of course, otherrepresentations can be used, such as a representation using a hue,saturation and intensity, or YUV co-ordinate system, or a subset ofcolour components in any colour space, for example only hue andsaturation in HSI.

The embodiment of the invention described above uses descriptors derivedfor images and groups of images. However, the image descriptors may befor regions of images, and similarly GoFGoP descriptors may be based onregions of images. Regions may be rectangular blocks, or regions ofdifferent shapes and sizes could be used. Alternatively, descriptors maybe derived for regions of the image corresponding to objects, forexample, a car, a house or a person. In either case, descriptors may bederived for all of the image or only part of it. Also, a GoFGoP groupdescriptor could be derived for a single image by applying the abovemethods to a plurality of regions in a image forming a group of imageregions.

In the search procedure, instead of inputting a simple colour query orselecting an image block, the user can, for example, use the pointingdevice to describe a region of an image, say, by encircling it,whereupon the control unit derives a descriptor for that region and usesit for searching in a similar manner as described above. Also, insteadof using images already stored in the image database for initiating asearch, an image could be input into the system using, for example, animage scanner or a digital camera. In order to perform a search in sucha situation, again the system first derives descriptors for the image orregions of the image, either automatically or as determined by the user.

Appropriate aspects of the invention can be implemented using hardwareor software.

In the above embodiments, the cluster distributions for eachrepresentative colour are approximated using Gaussian functions, and themean, variances and covariances of those functions are used in thedescriptor values. However, other functions or parameters can be used toapproximate the component distributions, for example, using basisfunctions such as sine and cosine, with descriptors based on thosefunctions.

1. A method of representing a group of images using at least oneprocessor to process signals corresponding to the images, the methodcomprising: obtaining image dominant colour representations for eachimage in the group of images, the dominant colour representationsexpressing the images in terms of one or more of dominant colour values,and where each of the image dominant colour representations has one ormore components including at least one image dominant colour value, anda variance indicating colour distribution variance of the image withrespect to the dominant colour value, said variance being calculated foreach image dominant colour using${CV}_{j} = {\frac{1}{N}\;{\sum\limits_{k = 0}^{N - 1}\;( {m_{j} - p_{kj}} )^{2}}}$where j indexes the color component, m_(j) is j-th component of theimage dominant color, P_(kj) is j-th component of the k-th pixel value,and the summation is over N pixels corresponding to the image dominantcolor under consideration; determining, using a processor, the values ofone or more colours that are dominant for the group of images bycombining the image dominant colour representations; generating a groupdominant colour representation for the group of images based on thecombined image dominant colour representation.
 2. A method of claim 1where each of the image dominant colour representations has one or morecomponents including optionally for each dominant colour value, a weightindicating the influence of the dominant colour in the image, the numberof dominant colours in the image, and spatial homogeneity of pixelscorresponding to the dominant colours in the image.
 3. A method asclaimed in claim 1 further comprising temporally or spatiallysub-sampling the group of images.
 4. A method as claimed in claim 3wherein images are omitted or included depending on their similarity toother images in the group.
 5. A descriptor for a group of images derivedby a method according to claim
 1. 6. A computer-readable storage mediumstoring computer-executable process steps for implementing a method asclaimed in claim
 1. 7. A computer system comprising: an image databasestoring images or groups of images; a descriptor database storingdescriptors for the images or groups of images in the image database;and a control unit programmed to perform the method as claimed inclaim
 1. 8. An apparatus for representing a group of images by derivinga group dominant colour representation of the group of images, whereinat least some of the images in the group of images are represented by arespective image dominant colour representation in terms of one or moredominant colour values for the image, and where each of the imagedominant colour representation has one or more components including atleast one image dominant colour value, and a variance indicating colourdistribution variance of the image with respect to the dominant colourvalue calculated for each image dominant colour using${CV}_{j} = {\frac{1}{N}\;{\sum\limits_{k = 0}^{N - 1}\;( {m_{j} - p_{kj}} )^{2}}}$where j indexes the color component, m_(j) is j-th component of thedominant color, P_(kj) is j-th component of the k-th pixel value, andthe summation is over N pixels corresponding to the dominant color underconsideration, the apparatus comprising a processor which derives agroup dominant colour representation of the group of images by combininga plurality of said image dominant colour representations.
 9. Theapparatus of claim 8 further comprising a storage unit storing at leastone of: one or more groups of images, one or more image dominant colourrepresentations, and one or more group dominant colour representations.